Comparison between MAP and postprocessed ML for image reconstruction in emission tomography when anatomical knowledge is available

TLDR: The analysis and simulations indicate that the postprocessing method is inferior, unless the noise correlations between neighboring pixels are taken into account, and it seems that MAP reconstruction is the more efficient method.

Abstract: Previously, the noise characteristics obtained with penalized-likelihood reconstruction [or maximum a posteriori (MAP)] have been compared to those obtained with postsmoothed maximum-likelihood (ML) reconstruction, for emission tomography applications requiring uniform resolution. It was found that penalized-likelihood reconstruction was not superior to postsmoothed ML. In this paper, a similar comparison is made, but now for applications where the noise suppression is tuned with anatomical information. It is assumed that limited but exact anatomical information is available. Two methods were compared. In the first method, the anatomical information is incorporated in the prior of a MAP-algorithm and is, therefore, imposed during MAP-reconstruction. The second method starts from an unconstrained ML-reconstruction, and imposes the anatomical information in a postprocessing step. The theoretical analysis was verified with simulations: small lesions were inserted in two different objects, and noisy PET data were produced and reconstructed with both methods. The resulting images were analyzed with bias-noise curves, and by computing the detection performance of the nonprewhitening observer and a channelized Hotelling observer. Our analysis and simulations indicate that the postprocessing method is inferior, unless the noise correlations between neighboring pixels are taken into account. This can be done by applying a so-called prewhitening filter. However, because the prewhitening filter is shift variant and object dependent, it seems that MAP reconstruction is the more efficient method.